Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
Soc Cogn Affect Neurosci. 2021 Aug 5;16(8):849-874. doi: 10.1093/scan/nsaa114.
Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.
鉴于大脑的动态性质，人们一直有动机超越“静态”功能连接，因为它描述了长时间内的功能相互作用。数据采集的进展和分析神经影像学方法的进步现在使我们能够使用 fMRI 评估整个大脑的动态功能连接（dFC）及其基于网络的类似物，即宏观尺度（mm）的动态功能网络连接。这导致了分析方法的快速发展，其中一些方法非常复杂，需要技术专业知识，这可能会令研究人员和神经科学家望而却步。同时，只有通过神经科学家和精神科医生等领域专家进行研究，才能真正取得进展，了解大脑活力与大脑疾病之间的关联。本文旨在对 dFC 的应用进行简要介绍。我们首先解释什么是 dFC 以及可以在什么情况下使用它。接下来，我们回顾了两种主要的分析方法类别来捕捉 dFC。我们讨论了 dFC 分析中的注意事项和考虑因素。最后，我们引导读者使用一个可公开访问的工具箱来捕捉 dFC 属性，并简要回顾使用该工具箱计算的一些动态指标。
